Computer-aided skin analysis has become widespread in the past decade with the availability of controlled lighting systems and sophisticated digital-image capture and processing capabilities. The skin analysis is typically limited to a particular area or region of interest (ROI) within a larger anatomical area by applying a mask thereto to delineate the ROI. The analysis often involves processing the ROI to identify and/or diagnose skin features, abnormalities or conditions such as skin color, hyperpigmented spots, wrinkles, skin texture, acne, rosacea, hyperpigmentation, and wrinkling, among others.
Because of the great emphasis placed on the appearance of the face, much computer-aided skin analysis work has focused on facial skin. There are a number of commercially available systems for facial skin imaging that can capture digital images in a controlled manner. These systems are often coupled to computer analysis systems for the visualization and quantification of visible skin features in standard white light images such as hyperpigmented spots, wrinkles, and texture, as well as non-visible features in hyperspectral absorption or fluorescence images such as UV spots and UV porphyrins. There are also a number of more sophisticated skin imaging systems such as the VISIA-CR imaging system, available from Canfield Scientific, Inc., that can provide multispectral images for the analysis of more complex skin conditions, such as acne.
Some limited work has been done in the design of skin masks for the delineation of skin ROIs to be analyzed. U.S. Pat. No. 6,571,003 to Hillebrand, et al., entitled “Skin imaging and analysis systems and methods,” describes a method of performing skin analysis within an operator designed ROI, i.e., a manually-designed polygon mask defining a skin patch created with the use of a computer. Guidelines for designing a skin mask for oblique-view face images by using some facial feature points as reference points are also described. The methods described therein, however, involve focused user interaction and are prone to user errors and inconsistencies in the masks designed by different users as well as masks designed by the same user.
U.S. Pat. No. 7,454,046, Chhibber, et al., entitled “Method and system for analyzing skin conditions using digital images,” describes a method to generate a skin map for computer analysis of visible skin features on a face image captured from a front-viewing angle. A crude mask is obtained by applying fixed thresholds to the R, G, and B channels of the color image. This mask is reduced by eliminating some of the non-skin pixels using a second threshold in one of the color spaces and further modified by a fixed size template mask. This method is prone to intensity changes in the image. As a result it may not be reproducible for follow-up visits and does not account for different skin types. Furthermore, this method does not provide a continuous map with well defined borders.
U.S. Pat. No. 7,233,693, Momma, et al., entitled “Methods and systems for computer analysis of skin image” describes a skin analysis system and method in which fixed shape circular masks—for front-view face images—are placed automatically on a subject's cheeks and a rectangular shape mask is placed on the subject's forehead and further adjusted by the user. These types of masks cover only a portion of the available skin in the region of interest such as the face, cheek and forehead, and are not well-fitted to the natural shape of the face.
U.S. Patent Application US2004/0028263 A1, Sakamato, et al., entitled “Digital zoom skin diagnostic apparatus,” describes a facial skin imaging and analysis system in which several fixed-size, small square patches (200×200 pixels) are manually placed on desired locations of the face. These patches cover only a small portion of the facial skin. As a result, computer analysis performed on these patches does not necessarily represent the skin conditions of the entire face.
Most other computer-aided skin analysis for clinical research studies utilizes manually designed skin masks based on a set of guidelines. However, the system operators interpret these guidelines subjectively. As a result, a high degree of variation arises in the mask design from one user to another. This manual process can also be tedious depending on the complexity of the ROI.
Moreover, in most clinical research studies, skin analysis algorithms are often performed on a collection of images in a batch mode. Prior to the analysis, a mask for each image needs to be designed manually for the desired skin ROI. The manual masking process for many images is time-consuming and once again prone to user errors. The errors and inconsistencies introduced by the user(s) in the mask design will have a negative impact on the overall analysis results, such as for example, on the comparability of analysis across different subjects or across different sessions for the same subject.
Image capture systems for computer-aided skin diagnoses often capture a number of images in several different imaging modalities such as standard white light, UV light with filters, blue-light, cross-polarized light, etc. Even though the images are usually captured in sequence with a minimal time delay, there is often a noticeable misalignment among these images because of the difficulty in keeping the subject perfectly still during the image capture process. This misalignment makes the mask designed for a skin site in one imaging modality not directly usable for the image of the same skin site captured in another imaging modality. The mask needs to be registered properly for the second image for meaningful comparison purposes. The registration of the mask can be performed manually, but this process is even more difficult than registering the masks in the same imaging modality because the visual comparison of images in different modalities is difficult for the human eye.
For most computer-aided skin analysis applications it is essential to use a mask designed for a baseline image of the skin site for a subsequent image of the same skin site captured in the same or another imaging modality. For quantitative comparison of skin analysis results, the ROI should cover the same areas for the two images of the same skin site. As mentioned, most often there is a misalignment between the images of the same skin site captured at different time points due to a change in the pose or expression of the subject. Consequently, for quantitative analysis purposes, a mask designed for the first captured image could not be directly usable for the second captured image. Even with controlled image capture systems, there can be significant misalignment in images of the same skin site captured at different points in time.
Some image processing systems offer some manual correction capability by allowing a user to visually inspect the mask overlaid on the first image and the same mask overlaid on the second image, and to adjust the mask. This manual correction process is time-consuming and prone to user errors. The misalignment issue also arises between images of a skin site captured with different imaging modalities.
Therefore, in view of the foregoing considerations, it is highly desirable to automate and standardize the process of designing the ROI or skin mask. Such an automated and standardized process can provide more meaningful and consistent skin analysis, eliminate user errors, and speed up the creation of the mask. Furthermore, it is highly desirable to use an ROI designed for an image of the skin site captured in one imaging session or imaging modality for another image of the same skin site captured in a subsequent session or another imaging modality.